Quantum Generative Adversarial Networks

GANs, Semi-Supervised GANs, Variational Autoencoders

Pethuru Raj, Amir Masoud Rahmani, Somenath Chakraborty, Ambika Paranthaman, A. Jayanthiladevi

• Publisher: De Gruyter
• Year: 2025
• eBook ISBN: 9783111355221
• Topic: Informatik
• Subtopic: IT-Industrie

GANs represent a generative model leveraging the growing power of deep learning (DL) algorithms. This book presents the breakthroughs, applications and latest research innovations of GANs and of quantum GANs. In particular, the book includes contributions on Autoencoders and Variational Autoencoders, Semi-Supervised GANs, Quantum GANs and Quantum Data Privacy, and Quantum GANs for image manipulation.

Table of contents

• Title Page
• Copyright
• Contents
• Frontmatter
• Contents
• List of contributing authors
• Chapter 1 A technical introduction of generative adversarial networks (GANs)
• Chapter 2 Generative adversarial networks: An outreach approach
• Chapter 3 A summary chapter on quantum generative adversarial networks (quantum GANs)
• Chpater 4 Quantum generative adversarial networks
• Chapter 5 Enhancing entrepreneurship in Industry 5.0 through generative AI
• Chapter 6 A novel approach to optimize power utilization and scheduling in dynamic networks through generative adversarial network-based prediction of network parameters
• Chapter 7 Different types of generative adversarial networks (GANs)
• Chapter 8 Adversarial networks in image generation: A detailed approach to manage datasets and to analyze discriminator and generator losses using GANs
• Chapter 9 Generative adversarial network (GAN) frameworks and platforms
• Chapter 10 GANs: applications and use cases
• Chapter 11 Innovations and potential impact of quantum generative adversarial networks in diverse applications
• Chapter 12 Generative adversarial networks’ transformative applications: From the visual arts to machine intelligence
• Chapter 13 A technical perspective on generative adversarial reinforcement learning
• Chapter 14 Quantum error mitigation for medical imaging noise reduction using GNNs
• Chapter 15 Safeguarding artificial intelligence: Ethical and security issues in quantum computing and GANs
• Chapter 16 Empowering cybersecurity: Exploring GANs and variants in intrusion detection
• Chapter 17 Quantum GANs: Revolutionizing data encoding for next-generation quantum applications
• Index
• Index